A Geometric Approach for Cross-View Human Action Recognition using Deep Learning
Antonios E. Papadakis, Eirini Mathe, Evaggelos Spyrou, Phivos Mylonas · 2019
In this paper we present an approach for the recognition of human actions which is based on a deep Convolutional Neural Network architecture. More specifically, 3D skeletal joint information is used to create 2D (image) representations. To compensate for potential viewpoint changes, these images are pre-processed using geometric transformations. Then, they are transformed to the spectral domain using well-known transforms. We focus on actions that are close to activities of daily living (ADLs), yet we evaluate our approach using a large-scale action dataset. We cover single-view, cross-view and cross subject cases and thoroughly discuss experimental results and the potential of our approach.